Own the technology that lets the world's most regulated enterprises trust AI agents in production.

This is the founding engineering leadership hire. You'll own the platform architecture end to end, build and lead the engineering team, and stay deeply hands-on — in the design and in the code — through our first wave of enterprise deployments. You'll also be in the room for technical due diligence with carrier CIOs and Chief AI Architects — the people deciding whether to put us into production. You'll report to the CEO and work side by side with them to set and execute the platform strategy, as a core member of the leadership team.

• CriticalBridge is an enterprise platform for building, governing, and running AI agents and automated workflows in regulated industries — starting with insurance, expanding to healthcare and finance.
• Every large enterprise is now accumulating AI agents faster than it can govern or connect them — built in different frameworks, owned by different teams, running as isolated silos. Agent sprawl has become a board-level problem, and new regulations carry hard deadlines. CriticalBridge is the neutral control plane that lets organizations design, deploy, govern, and orchestrate agents — native and third-party — inside their own security perimeter, with the audit trail and compliance controls regulated buyers require.
• We're a seed-stage company founded by operators who have led underwriting, IT, and large-scale transformation inside the world's largest carriers. We have live pilots underway with Tier-1 global insurance carriers. The product is real, in customers' hands, and the market timing is now.

• Personally own the end-to-end platform architecture.
• Stay hands-on. You'll spend significant time designing, writing, and reviewing code — not just directing it. You'll know the platform down to the codeline, and you set the bar by example.
• Hire, lead, and mentor the engineering team, and build the culture and processes that scale with us.
• Own the security and compliance posture for regulated verticals: NAIC-aligned controls, SOC 2 readiness, and compliance-grade governance — validation gates, human-in-the-loop enforcement, and audit infrastructure that survives a carrier's risk review.
• Drive delivery of our active pilots — turning scoping sessions into production deployments inside complex, legacy-heavy carrier environments.
• Own the agentic execution platform: agent orchestration, the LLM provider-abstraction layer, RAG and knowledge-graph retrieval, and the governance layer wrapping all of it.
• Make the build/buy/vendor calls, manage cloud cost and reliability, and own our GCP footprint.
• Work directly with the CEO to set and execute the platform strategy; partner with founders and customers on roadmap and technical due diligence.

• A sitting or recent CTO / VP Engineering from a SaaS software company, with a track record of personally architecting, building, and shipping commercial software platforms.
• Strong systems-architecture skills: you have personally designed and evolved cloud-native, multi-tenant SaaS platforms — service boundaries, tenant-isolation models, data architecture, API design — and can whiteboard and defend every major decision.
• Deep coding experience — current, not historical. Expert in TypeScript/Node.js; you still write production code, review PRs critically, and expect to own the platform architecture and every line of code that ships. Google Cloud experience strongly preferred.
• Real distributed-systems chops: queues, event-driven coordination, isolation models, data consistency, observability, and scale.
• Experience building for regulated or security-sensitive enterprises (insurance, finance, healthcare, or similar) would be helpful — SOC 2 / compliance, enterprise SSO, audit, and data-isolation requirements.
• Real AI product experience — you have built and shipped AI-enabled products that run in production for real users, not just used AI tools or prototyped demos. Hands-on with AI/LLM systems: agent orchestration, RAG, embeddings/vector search, prompt and eval pipelines, provider abstraction, and the reliability and cost trade-offs of running LLMs in production.
• Comfort being customer-facing in technical settings — you can earn the trust of a skeptical CIO or Chief AI Architect.
• Startup or 0→1 experience; thrives in ambiguity, sets priorities, and ships under real deadlines. You'd rather build the platform and be accountable for it than administer it.

TypeScript (strict) / Node.js on Google Cloud — Cloud Run, Pub/Sub, Postgres, BigQuery — with Terraform IaC. Electron/React desktop application and Next.js web. AI-native architecture: RAG over vector search plus a knowledge graph, and provider-abstracted LLMs (OpenAI / Anthropic / Google). OpenTelemetry-based observability throughout. We'll walk you through the full architecture during the interview process.

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